A Digital Twin Case Study on Prefabricated Component Factory for Offsite Manufacturing of Buildings

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Abstract

With the transformation of the construction manufacturing industry, off-site manufacturing technologies are gradually becoming mainstream. The existing digital systems of Prefabricated Component (PC) factories have problems such as incomplete presentation, inaccurate expression of physical entities in the information space, and weak real-time synchronization ability. To address these issues, we propose a digital twin system architecture applied to PC factories, which is divided into the physical entity layer, the digital twin entity layer, and the user entity layer. On this basis, relying on the Hannan PC Factory of China Construction Third Engineering Bureau Science and Technology Innovation Industry Development Co., Ltd., and based on physical systems such as the Internet of Things (IoT), Programmable Logic Controllers (PLC), central control systems, and Manufacturing Execution Systems (MES), a complete digital twin system has been designed and implemented. Through the association and mapping between digital twin virtual entities and physical entities, staff can monitor the production operation status in a realistic three-dimensional visual way in real time and make decisions. The actual operation effect has verified the effectiveness of the system framework, solved the problems that traditional PC factory digital systems cannot comprehensively present and accurately express the physical entities of PC factories and synchronize the production process in real time, and can lay a solid foundation for the subsequent dynamic prediction and decision optimization of prefabricated component production facilities.

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APA

Tan, Z., Tu, M., He, X., Chen, B., Zhao, Y., & Shang, J. (2025). A Digital Twin Case Study on Prefabricated Component Factory for Offsite Manufacturing of Buildings. In Proceedings of 2025 6th International Conference on Computer Information and Big Data Applications, CIBDA 2025 (pp. 416–421). Association for Computing Machinery, Inc. https://doi.org/10.1145/3746709.3746777

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